In mental state decoding, researchers aim to identify the set of mental states (eg,
experiencing happiness or fear) that can be reliably identified from the activity patterns of a …

Self-supervised learning techniques are celebrating immense success in natural language
processing (NLP) by enabling models to learn from broad language data at unprecedented …

This study presents a comprehensive examination of sex-related differences in resting-state
electroencephalogram (EEG) data, leveraging two different types of machine learning …

Significant changes in brain morphology occur during the third trimester of gestation. The
capability of deep learning in leveraging these morphological features has enhanced the …

Brain decoding that classifies cognitive states using the functional fluctuations of the brain
can provide insightful information for understanding the brain mechanisms of cognitive …

Brain dynamics are highly complex and yet hold the key to understanding brain function and
dysfunction. The dynamics captured by resting-state functional magnetic resonance imaging …

The application of artificial neural networks (ANNs) to functional magnetic resonance
imaging (fMRI) data has recently gained renewed attention for signal analysis, modeling the …

It may be possible to extract knowledge from functional magnetic resonance (fMRI) data with
artificial neural networks (ANNs) and explainable artificial intelligence (xAI). However …